Targeting of food aid programs: Evidence from Egypt
In-kind food aid programs remain prominent world-wide. Targeting in these programs is complex due to potential distortions in consumption. This paper advances the literature by moving beyond poverty-based targeting to address nutritional objectives. Using data from a randomized controlled trial (RCT...
| Autores principales: | , |
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| Formato: | Artículo preliminar |
| Lenguaje: | Inglés |
| Publicado: |
International Food Policy Research Institute
2025
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/179370 |
| _version_ | 1855537845327888384 |
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| author | Mahmoud, Mai Kurdi, Sikandra |
| author_browse | Kurdi, Sikandra Mahmoud, Mai |
| author_facet | Mahmoud, Mai Kurdi, Sikandra |
| author_sort | Mahmoud, Mai |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | In-kind food aid programs remain prominent world-wide. Targeting in these programs is complex due to potential distortions in consumption. This paper advances the literature by moving beyond poverty-based targeting to address nutritional objectives. Using data from a randomized controlled trial (RCT), we apply machine learning (ML) techniques to analyze heterogeneity in impacts across nutritional outcomes, aiming to inform targeting based on observable characteristics. We find that such characteristics significantly predict heterogeneity in treatment effects, though relevant predictors differ by outcome and treatment type. Building on recent literature advocating for balancing of deprivation and expected impact, we show that, in our context, the trade-off between targeting the most impacted versus the most deprived households is limited. Instead, the main challenge is prioritizing among competing nutritional objectives. Our findings indicate that ML methods can inform outcome-specific targeting criteria, though these criteria vary across outcomes and are imperfectly correlated. |
| format | Artículo preliminar |
| id | CGSpace179370 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | International Food Policy Research Institute |
| publisherStr | International Food Policy Research Institute |
| record_format | dspace |
| spelling | CGSpace1793702026-01-03T02:14:39Z Targeting of food aid programs: Evidence from Egypt Mahmoud, Mai Kurdi, Sikandra nutrition econometric models food aid machine learning targeting food aid In-kind food aid programs remain prominent world-wide. Targeting in these programs is complex due to potential distortions in consumption. This paper advances the literature by moving beyond poverty-based targeting to address nutritional objectives. Using data from a randomized controlled trial (RCT), we apply machine learning (ML) techniques to analyze heterogeneity in impacts across nutritional outcomes, aiming to inform targeting based on observable characteristics. We find that such characteristics significantly predict heterogeneity in treatment effects, though relevant predictors differ by outcome and treatment type. Building on recent literature advocating for balancing of deprivation and expected impact, we show that, in our context, the trade-off between targeting the most impacted versus the most deprived households is limited. Instead, the main challenge is prioritizing among competing nutritional objectives. Our findings indicate that ML methods can inform outcome-specific targeting criteria, though these criteria vary across outcomes and are imperfectly correlated. 2025-12-31 2026-01-02T22:02:22Z 2026-01-02T22:02:22Z Working Paper https://hdl.handle.net/10568/179370 en https://hdl.handle.net/10568/132231 Open Access application/pdf International Food Policy Research Institute Mahmoud, Mai; and Kurdi, Sikandra. 2025. Targeting of food aid programs: Evidence from Egypt. IFPRI Discussion Paper 2393. Washington, DC: International Food Policy Research Institute. https://hdl.handle.net/10568/179370 |
| spellingShingle | nutrition econometric models food aid machine learning targeting food aid Mahmoud, Mai Kurdi, Sikandra Targeting of food aid programs: Evidence from Egypt |
| title | Targeting of food aid programs: Evidence from Egypt |
| title_full | Targeting of food aid programs: Evidence from Egypt |
| title_fullStr | Targeting of food aid programs: Evidence from Egypt |
| title_full_unstemmed | Targeting of food aid programs: Evidence from Egypt |
| title_short | Targeting of food aid programs: Evidence from Egypt |
| title_sort | targeting of food aid programs evidence from egypt |
| topic | nutrition econometric models food aid machine learning targeting food aid |
| url | https://hdl.handle.net/10568/179370 |
| work_keys_str_mv | AT mahmoudmai targetingoffoodaidprogramsevidencefromegypt AT kurdisikandra targetingoffoodaidprogramsevidencefromegypt |